Multi-Input Multi-Output (MIMO) technology is emerging as the default choice for wireless networks. The wireless industry is continuously pushing toward increasing the number of antennas per device. While 3×3 MIMO nodes represented the state of the art in 2009, 4×4 MIMO nodes were introduced on the market in 2010. Simultaneously, there is a proliferation of wireless devices with diverse form factors. These range from large devices, like desktops and laptops, to small devices, like temperature or light sensors, and a whole range of devices in between like smartphones and tablets. The physical size of these devices intrinsically limits the maximum number of antennas that they can support, and their differing capabilities and costs mean that they will naturally have different MIMO processing power. The combination of these two trends—a growth in the maximum number of antennas per device, and an increase in device diversity—means that future wireless networks will be populated by heterogeneous access points (APs) and clients supporting different numbers of antennas. For example, today a home user may have a 2- or 3-antenna AP but one of her neighbors may have a single-antenna AP on the same channel. Even inside a single house, users can connect their high definition television to their video server using high-end 4×4 MIMO 802.11n devices, while continuing to use their 2- or 3-antenna wireless AP for the remaining devices in the home, while the home sensor network uses a single-antenna home controller that communicates with the sensors and actuators.
The existing design of 802.11n however uses the blueprint of traditional single-antenna networks, and as a result cannot efficiently support such heterogeneous MIMO networks. Consider for example the network 100 in FIG. 1 where a single-antenna pair 102 is exchanging a packet 103. A nearby 2×2 802.11n system 104 abstains from concurrently transmitting because it senses the medium as occupied. However, this is wasteful because a 2×2 MIMO pair can support two concurrent transmissions, and hence should be able to transmit a packet concurrently with the ongoing single-antenna transmission.
Recently, MIMO networks have attracted much attention from both the theoretical and empirical research communities. This resulted in new powerful theories including virtual MIMO and interference alignment and led to pioneering systems that expanded and validated the theory [5, 7, 1].
Past MIMO systems [1, 7, 5] have generally required concurrent transmissions to be pre-coded together at a single transmitter (as in beamforming [1]), decoded together at a single receiver (as in SAM [7]), or the transmitters or the receivers have to be controlled over the Ethernet by a single master node (as in IAC [5]).
Previous work on carrier sense in the presence of ongoing transmissions is related to packet detection in ZigZag [4] and carrier counting in SAM [7]. These schemes detect the number of concurrent transmissions using preamble correlation.
Some relevant theoretical work is past work on multi-user MIMO, interference alignment and cognitive MIMO. Multi-user MIMO allows multiple clients to be served simultaneously by a single base station that has more antennas than any of the clients. A number of techniques, such as beamforming, dirty paper coding, linear decorrelators, and successive interference cancellation, have been proposed to achieve the capacity of both the uplink and the downlink. More recently, work on interference alignment has shown new capacity results for multi-user MIMO channels. Finally, theoretical work on cognitive MIMO has advocated the use of MIMO to ensure that secondary users can coexist with the primary users, without creating interference to the primary users. These papers provide only theoretical solutions and typically target specific topologies.
References cited above include the following:                [1] Ehsan Aryafar and Naren Anand and Theodoros Salonidis and Edward Knightly. Design and Experimental Evaluation of Multi-User Beamforming in Wireless LANs. Mobicom, 2010.        [2] Jacksons Labs: Fury GPSDO, jackson-labs.com.        [3] System Description and Operating Principles for High Throughput Enhancements to 802.11. IEEE 802.11-04/0870r, 2004.        [4] Shyamnath Gollakota and Dina Katabi. ZigZag Decoding: Combating Hidden Terminals in Wireless Networks. Sigcomm, 2008.        [5] Shyamnath Gollakota and Samuel Perli and Dina Katabi. Interference Alignment and Cancelation. Sigcomm, 2009.        [6] Ettus Inc. Universal Software Radio Peripheral. http://ettus.com.        [7] Kun Tan and He Liu and Ji Fang and Wei Wang and Jiansong Zhang and Mi Chen and Geoffrey Voelker. SAM: Enabling Practical Spatial Multiple Access in Wireless LAN. Mobicom, 2009.        [8] D. Tse and P. Vishwanath. Fundamentals of Wireless Communications. Cambridge University Press, 2005.        [9] IEEE 802.11 WG. Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications. IEEE, 1999.        